Learning Bidirectional Global-to-Local Reranking for Generalized Category Discovery

Generalized Category Discovery (GCD) aims to identify both known and novel categories in an unlabeled image collection by exploiting supervision from a subset of known categories. Recent approaches improve representation learning by incorporating neighboring samples from a feature bank. However, these methods typically rely on predefined similarity measures for neighborhood construction, which may retrieve semantically unrelated instances and consequently provide unreliable learning signals. To address this limitation, we propose **G**lobal–**Lo**cal **Re**ranking Network (**GLoRe**), a learnable bidirectional ranking framework for GCD. Rather than treating feature similarity as a fixed indicator of semantic relevance, GLoRe learns to assess the relationship between a query and its retrieved candidates and uses this information to refine their ranking. By learning semantic relationships instead of fixed category predictions, GLoRe can transfer the learned ranking function from labeled examples to unlabeled instances belonging to both known and novel categories. Extensive experiments on fine-grained and coarse-grained benchmarks demonstrate that GLoRe achieves state-of-the-art performance on fine-grained datasets while remaining competitive with state-of-the-art methods on coarse-grained benchmarks. Our code will be made publicly available upon acceptance.